Smoothing L0- and L1-Norm regularizers and their relations to non-local means for CT reconstruction

2019 ◽  
Author(s):  
Yueyang Teng ◽  
Qing Guo ◽  
Ge Wang
Keyword(s):  
Ct Reconstruction ◽  
L1 Norm ◽  
Local Means ◽  
Non Local

Few-view CT reconstruction via a novel non-local means algorithm

2016 ◽  
Vol 32 (10) ◽  
pp. 1276-1283 ◽  
Author(s):  
Zijia Chen ◽  
Hongliang Qi ◽  
Shuyu Wu ◽  
Yuan Xu ◽  
Linghong Zhou
Keyword(s):  
Ct Reconstruction ◽  
Local Means ◽  
Non Local

MO-DE-207A-11: Sparse-View CT Reconstruction Via a Novel Non-Local Means Method

2016 ◽  
Vol 43 (6Part30) ◽  
pp. 3702-3703 ◽  
Author(s):  
Z Chen ◽  
H Qi ◽  
S Wu ◽  
Y Xu ◽  
L Zhou
Keyword(s):  
Ct Reconstruction ◽  
Local Means ◽  
Non Local

Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction

2016 ◽  
Vol 61 (18) ◽  
pp. 6878-6891 ◽  
Author(s):  
Hojin Kim ◽  
Josephine Chen ◽  
Adam Wang ◽  
Cynthia Chuang ◽  
Mareike Held ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Total Variation ◽  
Ct Reconstruction ◽  
L1 Norm ◽  
Non Local

Sparse angular CT reconstruction using non-local means based iterative-correction POCS

2011 ◽  
Vol 41 (4) ◽  
pp. 195-205 ◽  
Author(s):  
Jing Huang ◽  
Jianhua Ma ◽  
Nan Liu ◽  
Hua Zhang ◽  
Zhaoying Bian ◽  
...  
Keyword(s):  
Ct Reconstruction ◽  
Local Means ◽  
Iterative Correction ◽  
Non Local

scholarly journals Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

2021 ◽  
Vol 18 (6) ◽  
pp. 2903
Author(s):  
Seong-Hyeon Kang ◽  
Ji-Youn Kim
Keyword(s):  
Light Microscopy ◽  
Noise Reduction ◽  
Natural Image ◽  
Search Window ◽  
Local Means ◽  
Image Characteristics ◽  
Non Local ◽  
Spatial Quality ◽  
Evaluation Parameters ◽  
Color Channels

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

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